For images with heavy occlusion, multiple overlapping objects in the same bounding box may result in confusing instance contours from both real objects and occlusion boundaries.
The mask head design of Mask R-CNN and its variants [1]}, [2]}, [3]}, [4]}, [5]} in Figure REF  directly regress the occludee with a fully convolutional network, which neglects both the occluding instances and the overlapping relations between objects.
To mitigate this limitation, BCNet extends existing two stage instance segmentation methods, by adding an occlusion perception branch parallel to the traditional target prediction pipeline. Thus, the interactions between objects within the ROI region can be well considered during the mask regression stage.
